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- Benjamin, L. A., et al.
(author)
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Antiphospholipid antibodies and neurological manifestations in acute COVID-19: A single-centre cross-sectional study
- 2021
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In: Eclinicalmedicine. - : Elsevier BV. - 2589-5370. ; 39
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Journal article (peer-reviewed)abstract
- Background: A high prevalence of antiphospholipid antibodies has been reported in case series of patients with neurological manifestations and COVID-19; however, the pathogenicity of antiphospholipid antibodies in COVID-19 neurology remains unclear. Methods: This single-centre cross-sectional study included 106 adult patients: 30 hospitalised COVID-neurological cases, 47 non-neurological COVID-hospitalised controls, and 29 COVID-non-hospitalised controls, recruited between March and July 2020. We evaluated nine antiphospholipid antibodies: anticardiolipin antibodies [aCL] IgA, IgM, IgG; anti-beta-2 glycoprotein-1 [a beta(2)GPI] IgA, IgM, IgG; anti-phosphatidylserine/prothrombin [aPS/PT] IgM, IgG; and anti-domain I b2GPI (aD1 beta 2GPI) IgG. Findings: There was a high prevalence of antiphospholipid antibodies in the COVID-neurological (73.3%) and non-neurological COVID-hospitalised controls (76.6%) in contrast to the COVID-non-hospitalised controls (48.2%). aPS/PT IgG titres were significantly higher in the COVID-neurological group compared to both control groups (p < 0.001). Moderate-high titre of aPS/PT IgG was found in 2 out of 3 (67%) patients with acute disseminated encephalomyelitis [ADEM]. aPS/PT IgG titres negatively correlated with oxygen requirement (FiO(2) R=-0.15 p = 0.040) and was associated with venous thromboembolism (p = 0.043). In contrast, aCL IgA (p < 0.001) and IgG (p < 0.001) was associated with non-neurological COVID-hospitalised controls compared to the other groups and correlated positively with D-dimer and creatinine but negatively with FiO(2). Interpretation: Our findings show that aPS/PT IgG is associated with COVID-19-associated ADEM. In contrast, aCL IgA and IgG are seen much more frequently in non-neurological hospitalised patients with COVID-19. Characterisation of antiphospholipid antibody persistence and potential longitudinal clinical impact are required to guide appropriate management. (C) 2021 The Author(s). Published by Elsevier Ltd.
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- Lekman, Magnus, et al.
(author)
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The genetic interacting landscape of 63 candidate genes in Major Depressive Disorder : an explorative study
- 2014
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In: BioData Mining. - : Springer Science and Business Media LLC. - 1756-0381. ; 7, s. 19-
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Journal article (peer-reviewed)abstract
- Background: Genetic contributions to major depressive disorder (MDD) are thought to result from multiple genes interacting with each other. Different procedures have been proposed to detect such interactions. Which approach is best for explaining the risk of developing disease is unclear. This study sought to elucidate the genetic interaction landscape in candidate genes for MDD by conducting a SNP-SNP interaction analysis using an exhaustive search through 3,704 SNP-markers in 1,732 cases and 1,783 controls provided from the GAIN MDD study. We used three different methods to detect interactions, two logistic regressions models (multiplicative and additive) and one data mining and machine learning (MDR) approach. Results: Although none of the interaction survived correction for multiple comparisons, the results provide important information for future genetic interaction studies in complex disorders. Among the 0.5% most significant observations, none had been reported previously for risk to MDD. Within this group of interactions, less than 0.03% would have been detectable based on main effect approach or an a priori algorithm. We evaluated correlations among the three different models and conclude that all three algorithms detected the same interactions to a low degree. Although the top interactions had a surprisingly large effect size for MDD (e. g. additive dominant model P-uncorrected = 9.10E-9 with attributable proportion (AP) value = 0.58 and multiplicative recessive model with P-uncorrected = 6.95E-5 with odds ratio (OR estimated from beta 3) value = 4.99) the area under the curve (AUC) estimates were low (< 0.54). Moreover, the population attributable fraction (PAF) estimates were also low (< 0.15). Conclusions: We conclude that the top interactions on their own did not explain much of the genetic variance of MDD. The different statistical interaction methods we used in the present study did not identify the same pairs of interacting markers. Genetic interaction studies may uncover previously unsuspected effects that could provide novel insights into MDD risk, but much larger sample sizes are needed before this strategy can be powerfully applied.
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